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Building AI-ready finance teams, not just AI tools

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  Published in Business and Finance on Thursday, September 25th 2025 at 12:50 GMT by TechRadar
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Building AI‑Ready Finance Teams – Not Just AI Tools

In a world where artificial intelligence is reshaping every industry, finance is no longer an exception. TechRadar’s recent deep‑dive, “Building AI‑ready finance teams – not just AI tools,” outlines why organisations must look beyond software to the people and processes that will actually drive AI adoption. The article, which runs over a thousand words on the TechRadar website, argues that the real competitive edge lies in developing talent, culture, and governance frameworks that can harness AI’s full potential.

1. The Promise and the Pitfall of AI in Finance

The article opens with a sobering yet optimistic view: AI can automate routine reconciliations, flag fraud faster, and uncover insights from unstructured data that human analysts would miss. Yet, it warns that “AI is not a silver bullet” and cites a Gartner 2023 survey (link embedded in the article) showing that only 18 % of finance leaders feel fully confident about their AI strategy. The shortfall, the article says, stems from a mismatch between cutting‑edge tools and the workforce that must operate them.

2. The Three Pillars of an AI‑Ready Finance Function

TechRadar identifies three interlocking pillars that organisations need to nurture:

a) Data‑First Mindset

Finance teams traditionally rely on structured data housed in ERP systems. AI, however, thrives on diverse, high‑quality data. The article references a Deloitte whitepaper (linked) that recommends building “data lakes” and integrating data from CRM, procurement, and even external market feeds. A case study on a mid‑size manufacturing firm (link to the firm’s own AI blog) illustrates how a unified data layer cut forecasting errors by 27 %.

b) Skill & Culture Transformation

Up‑skilling is highlighted as the linchpin. The piece quotes Dr. Priya Nair, AI‑lead at the University of Oxford, who stresses that “people need to understand both the technical and ethical dimensions of AI.” TechRadar lists a range of programmes: online courses in machine‑learning basics, workshops on bias detection, and “shadow‑learning” sessions where finance staff pair with data scientists. It also references a McKinsey report (link provided) showing that firms that invest in AI literacy see a 3‑year ROI of 12 % higher profitability.

c) Governance & Ethical Frameworks

The article acknowledges that with great power comes great responsibility. It outlines a governance model that includes an AI ethics committee, clear data‑usage policies, and audit trails for AI decisions. A link to the EU AI Act is embedded, underscoring the need to align internal policies with emerging regulations.

3. From Tooling to Talent: Why Software Alone Is Insufficient

A recurring theme in the article is the danger of a “tool‑first” approach. TechRadar points out that many finance departments invest heavily in robotic process automation (RPA) and chat‑bot solutions, but fail to measure outcomes beyond cost savings. The article cites a study by Accenture (linked) which found that firms with mature AI governance were 2.5 times more likely to report tangible business value than those that relied solely on technology.

The piece also discusses “AI fluency” – a metric that gauges how well finance teams can interpret model outputs and translate them into business decisions. According to the article, organisations that adopt AI fluency programmes see a 30 % faster rollout of new AI features.

4. Cross‑Functional Collaboration: Finance Meets Data Science

TechRadar emphasises that AI success hinges on collaboration between finance, IT, and data science. The article includes a link to a PwC case study that describes how a global bank created a “Finance‑Data Science Co‑Lab” – a shared workspace where analysts, data engineers, and auditors iterated on models in real time. The result was a 40 % reduction in model drift over the first year.

The article also highlights the importance of “design thinking” in finance projects. By involving end‑users early, teams can uncover hidden constraints and shape models that fit business realities. A link to a design‑thinking toolkit is provided for readers interested in adopting this approach.

5. Recommendations for Building an AI‑Ready Finance Team

Towards the end, TechRadar distills its findings into a practical action plan:

1. Audit Existing Data – Identify data gaps and build a roadmap for enrichment.
2. Launch Pilot Projects – Start small with high‑impact use cases such as expense fraud detection.
3. Create Learning Loops – Pair finance staff with data scientists for joint training.
4. Establish Governance – Form an AI ethics board and define clear model‑governance processes.
5. Measure Impact – Track KPIs like forecast accuracy, cycle time, and ROI on AI initiatives.

The article quotes CFO of a UK fintech, Alex Green, who says, “Our AI readiness program didn’t just boost our bottom line; it changed how we think about risk.” Green’s firm reported a 22 % improvement in working‑capital management after adopting AI‑driven cash‑flow forecasting.

6. The Bottom Line

TechRadar’s piece makes a compelling case that the future of finance is not simply about installing the latest AI tool but about building an ecosystem where people, data, and governance align. The article’s blend of industry research, real‑world case studies, and actionable advice provides a roadmap for finance leaders who want to stay ahead of the curve. It also reminds readers that the most transformative AI deployments will be those that combine cutting‑edge technology with a culture of continuous learning and ethical responsibility.

For finance professionals looking to go beyond automation and into strategic AI innovation, the article serves as both a wake‑up call and a blueprint. By following its recommendations—starting with data quality, investing in skill development, and setting up robust governance—companies can ensure that AI becomes a true business partner, not just another tool in the toolkit.